Untitled

Sri Seshadri

3/19/2020

Objective

  1. Forecast next 5 years of US Sales.
  2. Quantify the likelihood of sales surpassing 1.8 trillion.
  3. Assess if forecasting sales by region would help the overall sales forecast.

Data

Variables Description
YearMonth 1992 Jan to 2019 Dec
RecessionYears 1, during the years 2001,2008 and 2009; 0 otherwise
RecessionCorrection 1, during the years 2001,2008,2009 and 2015-16;0 otherwise
Sales Sales in millions (USD)
Sales_in_trillions Sales in trillions (USD)

Total Business Sales in the US

Seasonality

Modeling issue 1: What should be the training and validation data?

Models trained with 2010-14 data would not learn the recession effect.

Demonstrate Issue 1

Demonstrate Issue 1 (contd)

What if the training data was to include 2015 data and the trained models be validated against 2016-19 data?

Modeling issue 2

Even if the models learnt the behavior at recession periods …

How do we know future recession years in the forecast horizon?

Solution options to modeling issue 1

  1. Leverage pre great recession data and its similarity to post great recession data.
  2. Asuume 2015 was a recession year and train models and test on 2016-2019.

Use Recession/Correction years as indicatior variables in each of the soluton strategy. Ignore great-recession data for model training.

Leverage pre-great recession data

Models trained with data from 1999-2003 when applied to 2015 data without retraining sufferes level/intercept mis-specification. The mis-specification is handled by adding the difference of sales in Dec 2003 and Dec 2014 to the forecast.

Leverage pre-great recession data

Models trained with data from 1999-2003 when applied to 2015 data without retraining sufferes level/intercept mis-specification. The mis-specification is handled by adding the difference of sales in Dec 2003 and Dec 2014 to the forecast.

Composite model

Models trained on pre-great recession data

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Accuracy metrics

.model .type ME RMSE MAE MPE MAPE MASE ACF1
LMArima Test -0.034 0.071 0.060 -2.792 4.533 1.036 0.655
composite Test -0.105 0.112 0.105 -7.737 7.737 1.818 0.467
tslm Test -0.164 0.173 0.164 -12.332 12.332 2.846 0.724

Assuming 2015 as recession year…

Accuracy metrics

.model .type ME RMSE MAE MPE MAPE MASE ACF1
composite Test -0.029 0.054 0.042 -2.092 3.041 0.736 0.506
LMArima Test 0.182 0.214 0.184 12.764 12.942 3.233 0.837
tslm Test -0.242 0.252 0.242 -17.384 17.384 4.244 0.788

Solution to issue 2: How do we forecast future recession

GDP output gap is the difference between GDP output and potential. Positive output gap indicates overheated market.

Educated guess that thre consecutive years of overheated market results in a recession the following year

Point forecasts for 2020-2024

Assumes 2020 is a recssion year

Likelihood for sales to surpass 1.8 trillion at least once

  1. Simulate 5 year forecasts based on bootstrapped validation errors.
  2. Simulate 5 year forecasts by modeling shocked time series data of sales.

Above methods simulate all possible recession scenarios (assuming utmost 1 year of recession is possible)

Simulation based on bootstrapped test errors

The chance of sales surpassing 1.8 trillion US dollars is 44%

Simulation based on shocked time series data of sales

The chance of sales surpassing 1.8 trillion US dollars is 40%